Field of Invention
The present invention relates to a predictive maintenance (PdM) method and a computer program product thereof. More particularly, the present invention relates to a baseline predictive maintenance (BPM) method for a target device (TD) based on virtual metrology (VM) and a computer program product thereof.
Description of Related Art
Production equipment is an essential part for any manufacturing factory. Failure of a component, a module, or a device (such as a heater, a pressure module, and a throttle valve, etc.) in the production equipment may cause production abnormalities which lead to poor product quality and/or low production capacity and thus cause significant losses.
In general, the most-commonly utilized approach for remedying the aforementioned problems is to perform scheduled preventive maintenance (PM). That is, to execute maintenance-related operations at a predetermined time interval. The predetermined time interval is basically decided according to the mean-time-between-failure (MTBF) of the target device (TD). As such, how to schedule a proper PM is usually a key issue for the factories. An improper scheduled PM may increase the maintenance cost or lower the production capacity.
To improve equipment maintenance programs for increasing fab performance, the International SEMATECH Manufacturing Initiative (ISMI) proposed an initiative of predictive and preventive maintenance (PPM). As defined by ISMI, PPM includes preventive maintenance (PM), condition-based maintenance (CbM), predictive maintenance (PdM), and breakdown maintenance (BDM). Among them, the ISMI claimed that CbM and PdM capabilities should be developed and available as an individual module or incremental modules so that an end user can choose to implement one, some, or all of the capabilities. CbM is defined as: “Maintenance is performed after one or more indicators show that equipment is going to fail or that equipment performance is deteriorating.” The technique of fault-detection-and-classification (FDC) is an approach related to CbM and is defined as: “Monitoring equipment and factory data to assess equipment health, and invoking warnings and/or tool shutdown when a fault is detected.” On the other hand, PdM is the technique of applying a predictive model to relate facility-state information to maintenance information for forecasting the remaining useful life (RUL) and the need for maintenance events that will alleviate unscheduled downtime.
Most conventional FDC approaches are to find out the TDs required for monitoring and the TDs' related key parameters needed to be monitored, and then by applying a statistical-process-control (SPC) approach to detect faults. Referring to FIG. 1, FIG. 1 is a SPC control chart of throttle-valve angles in a plasma-enhanced-chemical-vapor-deposition (PECVD) Tool, wherein a key parameter of the target device (TD) to be monitored is an angle of a throttle (i.e. throttle-valve angle). However, in a practical situation, abnormality of the throttle-valve angle may not be solely caused by itself; instead, it may also be due to the influence of other related parameters. As shown in FIG. 1, the central angle of the throttle-valve is 27 degrees; its upper control limit (UCL) and lower-control limit (LCL) are 32 and 22 degrees respectively as defined by maintenance engineers; and 450 samples in total are monitored. The conventional SPC method concludes that those samples in circles 1, 2 and 4 are outliers while the sample in circle 3 is within the control limit. After careful inspections, the samples in circles 2 and 4 are indeed abnormal and are caused by the throttle-valve's malfunction. As for the sample in circle 1, the abnormality is not caused by the throttle-valve itself, but is resulted from the deviation of the related-parameter “Ammonia (NH3)”. Also, the deflection shown in circle 3 is due to the deviation of the related-parameter “Tube Pressure”. As such, the conventional SPC method cannot detect and diagnose the faults at the samples in circles 1 and 3.
Hence, there is a need to provide baseline predictive maintenance (BPM) method for a target device (TD) and a computer program product thereof to overcome the disadvantages of the aforementioned conventional skills.